Method and algorithm for quantifying polynucleotides

ABSTRACT

Machine executable method of analyzing growth curve data to identify the transition from a baseline phase into a growth phase. Applications of the method include analysis of results from time-dependent monitoring of amplicon synthesis in a nucleic acid amplification reaction to quantify a starting amount of a nucleic acid template in a test sample. The method advantageously simplifies the quantitation by circumventing the need to establish thresholds used for calculating initiation of the growth phase, to calculate derivatives, or to perform linear regression analysis.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/693,455, filed Jun. 22, 2005. The entire disclosure of this priorapplication is hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to the field of biotechnology. Morespecifically, the invention relates to polynucleotide quantitation usingreal-time nucleic acid amplification.

BACKGROUND OF THE INVENTION

The subfield of molecular diagnostics which relates to nucleic acidquantitation has embraced kinetic analysis as a means for interpretingresults from nucleic acid amplification reactions. In these procedures,sometimes referred to as “real-time” amplification procedures, theamount of amplicon present in a nucleic acid amplification reactionmixture is monitored as a function of time. Ideally, the result of thismonitoring is a growth curve having a sigmoid shape, where an initialbaseline phase is followed by a growth phase, which is followed by aplateau phase.

Another important trend in the molecular diagnostics field is the drivetoward full automation of laboratory procedures and analysis of results.Fully automated real-time nucleic acid assays require machine executablealgorithms capable of analyzing data. In this regard, there is arequirement for data processing algorithms that accurately output anamount or concentration of a nucleic acid that gave rise to an observedamplification result.

Prior methods of automating the analysis of real-time amplificationreactions have relied on mathematical treatments of growth curves. Forexample, certain algorithms are based on the slope of the log-linearsegment of a growth curve, or on derivative-based analysis of most orall of the curve. Frequently, there is an additional requirement forcalculating a “threshold” value which must be exceeded to indicate atrue positive amplification result.

Unfortunately, the quantitative abilities of some prior methods foranalyzing results from real-time nucleic acid amplification reactionsare compromised at very low target levels (i.e., less than about 100copies/reaction). This may be due to a fanning pattern whichcharacterizes growth curves of reactions conducted using very low targetlevels, meaning that different reactions conducted using presumablyidentical initial target amounts produce amplicons at somewhat differentrates. Stated differently, the kinetics of amplification in reactionsconducted using very low template levels exhibit noticeable fluctuation,and this fluctuation leads to uncertainty in the final quantitativeresult generated by some algorithms. Methods that additionally requirecalculation of a threshold for indicating a positive result compoundthis uncertainty.

Thus, there exists a need for quantitative methods which are notseverely compromised by problems related to fluctuation in data derivedfrom real-time amplification of low levels of target.

SUMMARY OF THE INVENTION

A first aspect of the invention relates to a method for determining theamount of a nucleic acid in a test sample. In accordance with themethod, first there is a step for amplifying a predetermined locus ofthe nucleic acid in an in vitro amplification reaction to create anucleic acid amplification product. Next, there is a step fordetermining a value proportional to the amount of the nucleic acidamplification product present at different times during the in vitroamplification reaction, whereby there is created a collection oftime-dependent values that collectively define a growth curve. Next,there is a step for performing a vector analysis on at least a portionof the growth curve to identify a time-dependent feature of the growthcurve. This vector analysis includes steps for (a) establishing aplurality of pairs of first and second vectors at different points onthe time dimension of the growth curve using the collection oftime-dependent values, wherein each vector of a single pair of vectorsamong the plurality has the same origin, and wherein the head of thefirst vector and the head of the second vector of the single pair ofvectors among the plurality are each positioned at different points onthe growth curve. The next step in the vector analysis procedureinvolves identifying the time-dependent feature of the growth curveusing the plurality of pairs of first and second vectors established instep (a) of the vector analysis. Finally, the invented method includes astep for determining from the time-dependent feature of the growth curvethe amount of the nucleic acid in the test sample. In a preferredembodiment, step (b) of the vector analysis involves calculating foreach different pair of vectors the value of a parameter that isdependent on that pair of vectors. For example, this parameter may be anangle between the vectors. When this is the case, step (b) of the vectoranalysis may further involve identifying a position on the growth curveat which the angle between vectors is a minimum angle. Alternatively,step (b) of the vector analysis may further involve identifying aposition on the growth curve at which the angle between vectors is amaximum angle. In another preferred embodiment, each vector in a singlepair of vectors has a different magnitude in the time dimension of thegrowth curve, these vectors being directionally similar vectors. Instill another preferred embodiment, each vector in a single pair ofvectors has a different magnitude in the time dimension of the growthcurve, but are directionally opposed vectors. In yet another preferredembodiment, each vector in a single pair of vectors has the samemagnitude in the time dimension of the growth curve, these vectors beingdirectionally opposed vectors. In still yet another preferredembodiment, the invented method further includes a processing stepbefore the step for performing the vector analysis. This processing stepinvolves processing the collection of time-dependent values of the firstdetermining step by a smoothing function to result in smoothed numericaldata. When this is the case, the collection of time-dependent valuesused in step (a) of the vector analysis may be the smoothed numericaldata resulting from this processing step. In a preferred embodiment ofthe method that includes the processing step, the smoothing function iseither a moving average smoothing function, or a curve-fitting smoothingfunction. In another preferred embodiment of the method that includesthe processing step, step (b) of the vector analysis involvescalculating for each different pair of vectors the value of a parameterdependent on that pair of vectors. For example, this parameter may be anangle between the vectors. In another preferred embodiment of the methodthat includes the processing step, each vector in a single pair ofvectors has a different magnitude in the time dimension of the growthcurve, these vectors being directionally similar vectors. When this isthe case, step (b) of the vector analysis may involve calculating foreach pair of vectors the value of a parameter dependent on that pair ofvectors. In a highly preferred embodiment, this parameter is an anglebetween the vectors. In another preferred embodiment of the method thatincludes the processing step, each vector in a single pair of vectorshas a different magnitude in the time dimension of the growth curve,these vectors being directionally opposed vectors. When this is thecase, step (b) of the vector analysis may involve calculating for eachpair of vectors the value of a parameter dependent on that pair ofvectors. For example, this parameter may be an angle between thevectors. In another preferred embodiment of the method that includes theprocessing step, each vector in a single pair of vectors has the samemagnitude in the time dimension of the growth curve, these vectors beingdirectionally opposed vectors. When this is the case, step (b) of thevector analysis may involve calculating for each pair of vectors thevalue of a parameter dependent on that pair of vectors. For example,this parameter may be an angle between the vectors. Generally speaking,the in vitro amplification reaction in the amplifying step of theinvented method may be an isothermal in vitro amplification reaction.More preferably, the value proportional to the amount of the nucleicacid amplification product in the first determining step is afluorescence value. Still more preferably, the step for determining fromthe time-dependent feature of the growth curve involves comparing thetime-dependent feature of the growth curve with a standard calibrationcurve. Yet even more preferably, the step for performing the vectoranalysis is automated by a computer.

Another aspect of the invention relates to a method for quantifying anamount of a nucleic acid in a test sample. In accordance with themethod, first there is a step for contacting the test sample with anamplifying agent. Next, there is a step for amplifying a predeterminedlocus of the nucleic acid in an in vitro amplification reaction tocreate a nucleic acid amplification product. Next, there is a step fordetermining a value proportional to the amount of the nucleic acidamplification product present at different times during theamplification reaction. Optionally, there is a step for processing thedetermined values using a smoothing function to result in processedvalues. Next, there is a step for performing a vector analysis on either(a) the determined values if the optional processing step is omitted or(b) the processed values if the optional processing step is included.The object of the vector analysis is to identify a time-dependentfeature of the determined values or processed values. Finally, there isa step for calculating from the time-dependent feature the amount of thenucleic acid in the test sample.

Another aspect of the invention relates generally to a method ofidentifying a feature on a curve. In accordance with the method, firstthere is a step for obtaining numerical data for the curve. Next, thereis a step for establishing a plurality of pairs of first and secondvectors at different points on a first dimension of the curve using thenumerical data. Each vector of a single pair of vectors among theplurality has the same origin, and the head of the first vector and thehead of the second vector of the single pair of vectors among theplurality are each positioned at different points on the curve. Next,there is a step for identifying the feature on the curve using theplurality of pairs of first and second vectors from the establishingstep. Generally speaking, the steps for establishing the plurality ofvector pairs and identifying the curve feature are automated by acomputer. In a preferred embodiment, the step for identifying the curvefeature involves calculating for each pair of vectors among theplurality the value of a parameter dependent on that pair of vectors.For example, this parameter may be an angle between vectors. When thisis the case, the identifying step may further involve identifying aposition on the curve at which the angle between vectors is a minimumangle. Alternatively, the identifying step may further involveidentifying a position on the curve at which the angle between vectorsis a maximum angle. In another preferred embodiment, each vector in asingle pair of vectors has a different magnitude in the first dimensionof the curve, these vectors being directionally similar vectors. Instill another preferred embodiment, each vector in a single pair ofvectors has a different magnitude in the first dimension of the curve,these vectors being directionally opposed vectors. In yet anotherpreferred embodiment, each vector in a single pair of vectors has thesame magnitude in the first dimension of the curve, these vectors beingdirectionally opposed vectors. In still yet another preferredembodiment, the invented method further includes a step for processingthe numerical data from the obtaining step by a smoothing function toresult in smoothed numerical data. When this is the case, the numericaldata used in the step for establishing the plurality of pairs of vectorsis the smoothed numerical data resulting from the processing step. In apreferred embodiment of the method that includes the processing step,the smoothing function in the processing step is either a moving averagesmoothing function, or a curve-fitting smoothing function. In apreferred embodiment of the method that includes the processing step,the identifying step involves calculating for each pair of vectors thevalue of a parameter dependent on that pair of vectors. For example,this parameter may be an angle between the vectors. In a preferredembodiment of the method that includes the processing step, each vectorin a single pair of vectors has a different magnitude in the firstdimension of the curve, these vectors being directionally similarvectors. When this is the case, the identifying step may involvecalculating for each pair of vectors the value of a parameter dependenton that pair of vectors. For example, this parameter may be an anglebetween the vectors. In a preferred embodiment of the method thatincludes the processing step, each vector in a single pair of vectorshas a different magnitude in the first dimension of the curve, thesevectors being directionally opposed vectors. When this is the case, theidentifying step may involve calculating for each pair of vectors amongthe plurality the value of a parameter dependent on that pair ofvectors. For example, this parameter may be an angle between thevectors. In a preferred embodiment of the method that includes theprocessing step, each vector in a single pair of vectors has the samemagnitude in the first dimension of the curve, these vectors beingdirectionally opposed vectors. When this is the case, the identifyingstep may involve calculating for each pair of vectors among theplurality the value of a parameter dependent on that pair of vectors.For example, this parameter may be an angle between the vectors.

Definitions

The following terms have the following meanings for the purpose of thisdisclosure, unless expressly stated to the contrary herein.

As used herein, a “test sample” is any sample to be investigated for thepresence of a particular polynucleotide species. Test samples includeany tissue or polynucleotide-containing material obtained from a human,animal, environmental, laboratory-derived or synthetic sample.

As used herein, “polynucleotide” means either RNA, DNA, or a chimericmolecule containing both RNA and DNA.

An “analyte amplicon” is a polynucleotide product of an amplificationreaction wherein an analyte polynucleotide served as the template forsynthesis of polynucleotide copies or amplification products.

An “analyte polynucleotide” is a target polynucleotide that is to bereplicated by a nucleic acid amplification reaction.

As used herein, a “detectable label” is a chemical species that can bedetected or can lead to a detectable response. Detectable labels inaccordance with the invention can be linked to polynucleotide probeseither directly or indirectly, and include radioisotopes, enzymes,haptens, chromophores such as dyes or particles that impart a detectablecolor (e.g., latex beads or metal particles), luminescent compounds(e.g., bioluminescent, phosphorescent or chemiluminescent moieties) andfluorescent compounds. Fluorescent dyes that bind specifically todouble-stranded DNA are still other examples of detection labels.

A “homogeneous assay” refers to a detection procedure that does notrequire physical separation of hybridized probe from non-hybridizedprobe prior to determining the extent of specific probe hybridization.Exemplary homogeneous assays, such as those described herein, can employmolecular beacons, molecular torches or other self-reporting probeswhich emit fluorescent signals when hybridized to an appropriate target,as well as other homogeneously detectable labels that will be familiarto those having an ordinary level of skill in the art.

As used herein, the terms “amplification” or “nucleic acidamplification” and variants thereof refer to in vitro procedures forobtaining multiple copies of a target nucleic acid sequence, itscomplement or fragments thereof. Generally speaking, amplification meansincreasing the number of these copies by at least 10 fold when comparedwith an initial amount present in a test sample.

As used herein, “thermal cycling” refers to repeated changes oftemperature, (i.e., increases or decreases of temperature) in a reactionmixture. Samples undergoing thermal cycling may shift from onetemperature to another, stabilize at that temperature, transition to asecond temperature or return to the starting temperature. Thetemperature cycle may be repeated as many times as required to study orcomplete the particular chemical reaction of interest.

By “target nucleic acid” or “target” is meant a nucleic acid containinga target nucleic acid sequence. In general, a target nucleic acidsequence that is to be amplified will be positioned between twooppositely disposed oligonucleotides, and will include the portion ofthe target nucleic acid that is complementary to each of theoligonucleotides.

By “target nucleic acid sequence” or “target sequence” or “targetregion” is meant a specific deoxyribonucleotide or ribonucleotidesequence comprising all or part of the nucleotide sequence of asingle-stranded nucleic acid molecule, and the deoxyribonucleotide orribonucleotide sequence complementary thereto.

By “transcription associated amplification” is meant any type of nucleicacid amplification that uses an RNA polymerase to produce multiple RNAtranscripts from a nucleic acid template. Conventionally, theseamplification reactions employ at least one primer having a 3′-end thatcan be extended by the activity of a DNA polymerase. One example of atranscription associated amplification method, called “TranscriptionMediated Amplification” (TMA), generally employs an RNA polymerase, aDNA polymerase, deoxyribonucleoside triphosphates, ribonucleosidetriphosphates, and a promoter-template complementary oligonucleotide,and optionally may include one or more analogous oligonucleotides.Variations of TMA are well known in the art as disclosed in detail inBurg et al., U.S. Pat. No. 5,437,990; Kacian et al., U.S. Pat. Nos.5,399,491 and 5,554,516; Kacian et al., PCT No. WO 93/22461; Gingeras etal., PCT No. WO 88/01302; Gingeras et al., PCT No. WO 88/10315; Malek etal., U.S. Pat. No. 5,130,238; Urdea et al., U.S. Pat. Nos. 4,868,105 and5,124,246; McDonough et al., PCT No. WO 94/03472; and Ryder et al., PCTNo. WO 95/03430. The methods of Kacian et al. are preferred forconducting nucleic acid amplification procedures of the type disclosedherein.

As used herein, an “oligonucleotide” or “oligomer” is a polymeric chainof at least two, generally between about five and about 100, chemicalsubunits, each subunit comprising a nucleotide base moiety, a sugarmoiety, and a linking moiety that joins the subunits in a linear spacialconfiguration. Common nucleotide base moieties are guanine (G), adenine(A), cytosine (C), thymine (T) and uracil (U), although other rare ormodified nucleotide bases able to hydrogen bond are well known to thoseskilled in the art. Oligonucleotides may optionally include analogs ofany of the sugar moieties, the base moieties, and the backboneconstituents. Preferred oligonucleotides of the present invention fallin a size range of about 10 to about 100 residues. Oligonucleotides maybe purified from naturally occurring sources, but preferably aresynthesized using any of a variety of well known enzymatic or chemicalmethods.

As used herein, a “probe” is an oligonucleotide that hybridizesspecifically to a target sequence in a nucleic acid, preferably in anamplified nucleic acid, under conditions that promote hybridization, toform a detectable hybrid. A probe optionally may contain a detectablemoiety which either may be attached to the end(s) of the probe or may beinternal. Detection may either be direct (i.e., resulting from a probehybridizing directly to the target sequence or amplified nucleic acid)or indirect (i.e., resulting from a probe hybridizing to an intermediatemolecular structure that links the probe to the target sequence oramplified nucleic acid). The “target” of a probe generally refers to asequence contained within an amplified nucleic acid sequence whichhybridizes specifically to at least a portion of a probe oligonucleotideusing standard hydrogen bonding (i.e., base pairing). A probe maycomprise target-specific sequences and optionally other sequences thatare non-complementary to the target sequence that is to be detected.

As used herein, an “amplification primer” is an oligonucleotide thathybridizes to a target nucleic acid, or its complement, and participatesin a nucleic acid amplification reaction. For example, someamplification primers, or more simply “primers,” may be optionallymodified oligonucleotides which are capable of hybridizing to a templatenucleic acid and which have a 3′ end that can be extended by a DNApolymerase activity. In general, a primer will have a downstreamtarget-complementary sequence, and optionally an upstream sequence thatis not complementary to target nucleic acids. The optional upstreamsequence may, for example, serve as an RNA polymerase promoter orcontain restriction endonuclease cleavage sites.

As used herein, “time-dependent” monitoring of nucleic acidamplification, or monitoring of nucleic acid amplification in“real-time” refers to a process wherein an indicator of the amount ofamplicon present in a nucleic acid amplification reaction is measured asa function of time and then used to determine a starting amount oftemplate that was present in the reaction mixture at the time theamplification reaction was initiated. For example, the amount ofamplicon can be measured prior to commencing each complete cycle of anamplification reaction that comprises thermal cycling, such as PCR.Alternatively, isothermal amplification reactions that do not requirephysical intervention to initiate the transtitions between amplificationcycles can be monitored continuously, or at regular time intervals(i.e., “read-cycles”) to obtain information regarding the amount ofamplicon present as a function of time.

As used herein, a “growth curve” refers to the characteristic pattern ofappearance of a synthetic product, such as an amplicon, in a reaction asa function of time or cycle number. A growth curve is convenientlyrepresented as a two-dimensional plot of time (x-axis) against someindicator of product amount, such as a fluorescence measurement(y-axis). Thus, growth curves conventionally have a time dimensionrepresented on one axis. Some, but not all, growth curves have asigmoid-shape.

As used herein, the “baseline phase” of a growth curve refers to theinitial phase of the curve wherein the amount of product (such as anamplicon) increases at a substantially constant rate, this rate beingless than the rate of increase characteristic of the growth phase (whichmay have a log-linear profile) of the growth curve. The baseline phaseof a growth curve typically has a very shallow slope, frequentlyapproximating zero.

As used herein, the “growth phase” of a growth curve refers to theportion of the curve wherein the measurable product substantiallyincreases with time. Transition from the baseline phase into the growthphase in a typical nucleic acid amplification reaction is characterizedby the appearance of amplicon at a rate that increases with time.Transition from the growth phase to the plateau phase of the growthcurve begins at an inflection point where the rate of ampliconappearance begins to decrease.

As used herein, the “plateau phase” of a triphasic growth curve refersto the final phase of the curve. In the plateau phase, the rate ofmeasurable product formation is substantially lower than the rate ofamplicon production in the log-linear phase, and may even approach zero.

As used herein, a “vector” refers to a mathematical quantity which isdescribed by both magnitude and direction. Vectors can be represented byuse of a scaled vector diagram. On such a diagram, an arrow is drawn torepresent the vector, with the vector having an obvious head and tail.The vector tail position is sometimes referred to as the “origin” of thevector. The magnitude of a vector is indicated by its length.

By “inflection” is meant a bending or curving.

By “inflection point” is meant a point on a curve at which there is achange in curvature from convex to concave or from concave to convex.

By “concave” is meant curved like the inside of a circle or sphere.

By “convex” is meant curved like the outside of a circle or sphere.

By “consisting essentially of” is meant that additional component(s),composition(s) or method step(s) that do not materially change the basicand novel characteristics of the present invention may be included inthe present invention. Any component(s), composition(s), or methodstep(s) that have a material effect on the basic and novelcharacteristics of the present invention would fall outside of thisterm.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B illustrate actual growth curves. FIG. 1A shows a biphasicgrowth curve having a baseline phase and a growth phase. FIG. 1B shows atriphasic growth curve having a baseline phase, a log-linear growthphase, and a plateau phase. Each distinct phase is separated fromanother by “transitions” which are illustrated by dotted circles. Thetransitions between the baseline phases and the growth phases exhibitconcave curvature, and the transition between the growth phase and theplateau phase exhibits convex curvature. The maximum of the firstderivative is indicated by “A” for each growth curve. The maximum of thesecond derivative is indicated by “B” for each growth curve.

FIG. 2 illustrates a paired set of two vectors (vectors “a” and “b”)drawn on a portion of a growth curve. The angle between the two vectorsis identified as ω.

FIG. 3 illustrates a portion of a growth curve with three paired sets ofvectors superimposed thereon at different positions. Each vector pair(i.e., vector a and vector b) is identified by a numerical subscript.The middle pair of vectors is shown as defining an angle (ω) that isgreater than the angle between the other vector pairs.

FIGS. 4A-4B illustrates the relationship between calculated values forangle ω and the remaining portion of a circle. Panel A illustrates twovectors that are directionally similar. Panel B illustrates two vectorsthat are directionally opposed.

FIGS. 5A-5B schematically illustrate differences between vector-basedalgorithms employing directionally similar vectors (panel A) anddirectionally opposed vectors (panel B). In this illustration,“fluorescence” (y-axis) is proportional to the amount of a nucleic acidamplification product present in an in vitro amplification reaction. Thetime value associated with the point on a growth curve at which theangle between directionally similar vectors becomes a maximum (see panelA) is referred to herein as “TArc.” The time value associated with thepoint on a growth curve at which the angle between directionally opposedvectors becomes a minimum (see panel B) is referred to herein as“OTArc.”

FIG. 6 shows a growth curve representing actual results obtained in anucleic acid amplification reaction. The growth curve presents rawexperimental results that did not include smoothing. Superimposed on thegrowth curve are indicators showing the positions for TArc, OTArc, themaximum of the second derivative, and the maximum of the firstderivative.

FIG. 7 shows a series of growth curves representing the amount ofnucleic acid amplification product (measured by fluorescence signal) asa function of time for nucleic acid amplification reactions conductedusing varying amounts of input HIV-1 analyte polynucleotide (indicatedby “copies/reaction”).

FIG. 8 is a graph showing results from a vector-based analysis of theraw experimental data appearing in FIG. 7 (no smoothing function). Thegraph relates the amount of an HIV-1 analyte polynucleotide standardthat was input into a real-time nucleic acid amplification reaction(x-axis) and the calculated TArc time (y-axis). Data points (filleddiamonds) represent calculated TArc values for individual growth curves.

FIG. 9 is a graph showing results from a vector-based analysis ofexperimental data appearing in FIG. 7 after processing using a movingaverage smoothing function. The graph relates the amount of an HIV-1analyte polynucleotide standard that was input into a real-time nucleicacid amplification reaction (x-axis) and the calculated TArc time(y-axis). Data points (filled diamonds) represent calculated TArc valuesfor individual growth curves.

FIG. 10 is a graph showing results from a vector-based analysis ofexperimental data appearing in FIG. 7 after processing using acurve-fitting smoothing function (equation 3, below). The graph relatesthe amount of an HIV-1 analyte polynucleotide standard that was inputinto a real-time nucleic acid amplification reaction (x-axis) and thecalculated TArc time (y-axis). Data points (filled diamonds) representcalculated TArc values for individual growth curves.

FIG. 11 is a graph showing results from a vector-based analysis ofexperimental data appearing in FIG. 7 after processing using acurve-fitting smoothing function (equation 4, below). The graph relatesthe amount of an HIV-1 analyte polynucleotide standard that was inputinto a real-time nucleic acid amplification reaction (x-axis) and thecalculated TArc time (y-axis). Data points (filled diamonds) representcalculated TArc values for individual growth curves.

FIG. 12 is a graph showing results from a vector-based analysis ofexperimental data appearing in FIG. 7 after processing using acurve-fitting smoothing function (equation 5, below). The graph relatesthe amount of an HIV-1 analyte polynucleotide standard that was inputinto a real-time nucleic acid amplification reaction (x-axis) and thecalculated TArc time (y-axis). Data points (filled diamonds) representcalculated TArc values for individual growth curves.

FIG. 13 shows a series of growth curves representing the amount ofnucleic acid amplification product (measured by fluorescence signal)synthesized as a function of time for nucleic acid amplificationreactions conducted using 5,000 copies/reaction of an analytepolynucleotide. The horizontal, heavy dashed line arbitrarily set at0.10 fluorescence units indicates points on the growth curves that wouldbe identified using a threshold-based algorithm to indicate the time atwhich a fluorescent signal was scored as positive. The inclined dottedline indicates points on the growth curves that would be identifiedusing the vector-based algorithm for identifying TArc values.

DETAILED DESCRIPTION OF THE INVENTION

Introduction and Overview

Herein there are disclosed machine executable algorithms for analyzingcurves represented by experimental data, such as that encountered in thetime-dependent monitoring of nucleic acid amplification, or bymathematical functions. Rather than relying on analysis of thelog-linear phase of a growth curve, or on identifying the time at whichthe amount of amplicon synthesized in the amplification reaction exceedsa threshold level, the invention employs a two-dimensional vectoranalysis to identify a feature corresponding to initiation of the upwardconcave curvature in a growth curve. Preferably, this identified featureis located between the baseline phase and the growth phase of the growthcurve.

Testing of the vector-based analytical method showed that quantificationof low levels of input target was superior when compared with analternative algorithm based on linear regression analysis of thelog-linear phase of the growth curve, and determination of the time atwhich the amplification signal exceeded a background threshold value.Such an alternative algorithm is described in the U.S. patentapplication having Ser. No. 60/659,874.

Still further, the disclosed analytical method advantageously eliminatesthe need for calculating derivatives or otherwise determining the slopeof a portion of a growth curve, and further eliminates the need forestablishing threshold values used for interpolating the amount ofamplicon. Indeed, the disclosed methods and algorithms, which rely onthe relationship between two vectors drawn on a curve, or on the datapoints representing a curve, exhibit excellent precision where differentamplification chemistries may be involved such that non-ideal growthcurves result.

PREFERRED EMBODIMENTS OF THE INVENTION

In preferred embodiments, the disclosed algorithms are used foranalyzing results obtained from time-dependent monitoring of ampliconsynthesis in a nucleic acid amplification reaction. As commonlypracticed, a detectable signal indicating the quantity of an analyteamplicon synthesized in a nucleic acid amplification reaction ismonitored at different times during the course of the reaction.Amplification is generally effected by the activity of oligonucleotidesthat have defined sequences, and that amplify a predetermined locus ofthe nucleic acid target. The detectable signal may be, for example, afluorescent signal, an electronic signal, an electrochemical orelectrochemiluminescent signal. The detectable signal may be monitoredat regular cycle intervals, or at regular or continuous time intervals.

Simply stated, the disclosed algorithms involve performing a vectoranalysis of the data to be analyzed in order to identify a feature on agrowth curve. Using this approach, which preferably involves identifyingat least one feature (such as an angle) which characterizes therelationship between two vectors established using the experimentaldata, there is eliminated any need for employing a threshold to indicateinitiation of a growth phase. In a preferred embodiment, the disclosedmethod involves performing a vector analysis on at least a portion of agrowth curve representing time-dependent readings of amplicon synthesis.The vector analysis involves establishing a plurality of pairs ofvectors at different points on the time axis of the growth curve. Eachvector in a single pair shares the same origin, and the heads of the twovectors in the pair are positioned at different points on the growthcurve (see FIGS. 5A and 5B). The time-dependent feature of the growthcurve can be identified using the plurality of pairs of first and secondvectors established in this manner. In certain embodiments, prior toperforming the vector analysis there is an optional step forcurve-fitting or “smoothing” of values representing amplicon synthesisin the growth curve. In this instance the vector analysis preferablycarried out using the smoothed or “fitted” curve instead of raw data.The products of these steps can easily be used for creating standardcurves, or for determining a feature of a growth curve representing anamplification reaction conducted using a test sample. In this way, thestarting amount of polynucleotide present in the test sample can bequantified.

Useful Amplification Methods

Examples of amplification methods useful in connection with the presentinvention include: Transcription Mediated Amplification (TMA), NucleicAcid Sequence-Based Amplification (NASBA), the Polymerase Chain Reaction(PCR), Strand Displacement Amplification (SDA), Self-Sustained SequenceReplication (3SR), DNA Ligase Chain Reaction (LCR) and amplificationmethods using self-replicating polynucleotide molecules and replicationenzymes such as MDV-1 RNA and Q-beta enzyme. Methods for carrying outthese various amplification techniques respectively can be found in U.S.Pat. No. 5,399,491, published European patent application EP 0 525 882,U.S. Pat. No. 4,965,188, U.S. Pat. No. 5,455,166, Guatelli et al., Proc.Natl. Acad. Sci. USA 87:1874-1878 (1990), International Publication No.WO 89/09835, U.S. Pat. No. 5,472,840 and Lizardi et al., TrendsBiotechnol. 9:53-58 (1991). The disclosures of these documents whichdescribe how to perform nucleic acid amplification reactions are herebyincorporated by reference.

Of course, other methods of amplifying nucleic acids can also beanalyzed using the algorithms and methods disclosed herein. Indeed,certain preferred embodiments employ time-dependent monitoring ofamplification reactions that require only a single extendable primer,and particularly include transcription-associated amplification systemsthat employ a single extendable primer in combination with a 3′-blockedoligonucleotide. Exemplary nucleic acid amplification methods based onthe use of a single extendable primer are presented in U.S. patentapplication Ser. No. 11/213,519, filed Aug. 26, 2005 and published Mar.2, 2006. The disclosure of this application is incorporated by referenceherein.

In another preferred embodiment of the invention, nucleic acid sequencesare amplified using a TMA protocol. According to this protocol, thereverse transcriptase which provides the DNA polymerase activity alsopossesses an endogenous RNase H activity. One of the primers used inthis procedure contains a promoter sequence positioned upstream of asequence that is complementary to one strand of a target nucleic acidthat is to be amplified. In the first step of the amplification, apromoter-primer hybridizes to the target RNA at a defined site. Reversetranscriptase creates a complementary DNA copy of the target RNA byextension from the 3′ end of the promoter-primer. Following interactionof an opposite strand primer with the newly synthesized DNA strand, asecond strand of DNA is synthesized from the end of the primer byreverse transcriptase, thereby creating a double-stranded DNA molecule.RNA polymerase recognizes the promoter sequence in this double-strandedDNA template and initiates transcription. Each of the newly synthesizedRNA amplicons re-enters the TMA process and serves as a template for anew round of replication, thereby leading to an exponential expansion ofthe RNA amplicon. Since each of the DNA templates can make 100-1000copies of RNA amplicon, this expansion can result in the production of10 billion amplicons in less than one hour. The entire process isautocatalytic and is performed at a constant temperature.

Useful Probe Labeling Systems and Detectable Moieties

Preferred detectable labels for probes in accordance with the presentinvention are detectable in homogeneous assay systems (i.e., where, in amixture, bound labeled probe exhibits a detectable change, such asstability or differential degradation, compared to unbound labeledprobe).

Essentially any labeling and detection system that can be used formonitoring specific nucleic acid hybridization can be used inconjunction with the present invention. Included among the collection ofuseful labels are fluorescent moieties (either alone or in combinationwith “quencher” moieties), chemiluminescent molecules, and redox-activemoieties that are amenable to electronic detection methods. In someembodiments, preferred fluorescent labels include non-covalently bindinglabels (e.g., intercalating dyes) such as ethidium bromide, propidiumbromide, chromomycin, acridine orange, and the like. In otherembodiments, the use of covalently bound fluorescent agents ispreferred. Preferred chemiluminescent molecules include acridiniumesters. Examples of these detectable labels have been disclosed byArnold et al., in U.S. Pat. No. 5,283,174 for use in connection withhomogenous protection assays. Preferred electronic labeling anddetection approaches are disclosed in U.S. Pat. Nos. 5,591,578 and5,770,369, and the published international patent application WO98/57158. Redox active moieties useful as labels in the presentinvention include transition metals such as Cd, Mg, Cu, Co, Pd, Zn, Feand Ru.

In some applications, probes exhibiting at least some degree ofself-complementarity are desirable to facilitate detection ofprobe:target duplexes in a test sample without first requiring theremoval of unhybridized probe prior to detection. By way of example,structures referred to as “molecular torches” and “molecular beacons”are designed to include distinct regions of self-complementarity andregions of target-complementarity. Molecular torches are fully describedin U.S. Pat. No. 6,361,945, and molecular beacons are fully described inU.S. Pat. No. 5,925,517, the disclosure of these patents beingincorporated by reference herein. Both of these self-reporting probesinclude a label pair that interacts when the probe is in a closedconformation in the absence of any hybridized target. Hybridization ofthe target nucleic acid and the target complementary sequence separatesthe self-complementary portions of the probes, thereby shifting theprobes to an open conformation. The shift to the open conformation isdetectable due to reduced interaction of the label pair, which may be,for example, a fluorophore and a quencher (e.g., DABCYL and EDANS).

Molecular torches, molecular beacons and the like preferably areindividually labeled with an interactive pair of detectable labels.Examples of detectable labels that are preferred as members of aninteractive pair of labels interact with each other by FRET or non-FRETenergy transfer mechanisms. Fluorescence resonance energy transfer(FRET) involves the radiationless transmission of energy quanta from thesite of absorption to the site of its utilization in the molecule, orsystem of molecules, by resonance interaction between chromophores, overdistances considerably greater than interatomic distances, withoutconversion to thermal energy, and without the donor and acceptor cominginto kinetic collision. The “donor” is the moiety that initially absorbsthe energy, and the “acceptor” is the moiety to which the energy issubsequently transferred. In addition to FRET, there are at least threeother “non-FRET” energy transfer processes by which excitation energycan be transferred from a donor to an acceptor molecule.

Examples of donor/acceptor label pairs that may be used in connectionwith the invention, making no attempt to distinguish FRET from non-FRETpairs, include fluorescein/tetramethylrhodamine, IAEDANS/fluororescein,EDANS/DABCYL, coumarin/DABCYL, fluorescein/fluorescein, BODIPY FL/BODIPYFL, fluorescein/DABCYL, lucifer yellow/DABCYL, BODIPY/DABCYL,eosine/DABCYL, erythrosine/DABCYL, tetramethylrhodamine/DABCYL, TexasRed/DABCYL, CY5/BH1, CY5/BH2, CY3/BH1, CY3/BH2 and fluorescein/QSY7 dye.Those having an ordinary level of skill in the art will understand thatwhen donor and acceptor dyes are different, energy transfer can bedetected by the appearance of sensitized fluorescence of the acceptor orby quenching of donor fluorescence. When the donor and acceptor speciesare the same, energy can be detected by the resulting fluorescencedepolarization. Non-fluorescent acceptors such as DABCYL and the QSY 7dyes advantageously eliminate the potential problem of backgroundfluorescence resulting from direct (i.e., non-sensitized) acceptorexcitation. Preferred fluorophore moieties that can be used as onemember of a donor-acceptor pair include fluorescein, ROX, TAMARA, andthe CY dyes (such as CY5). Highly preferred quencher moieties that canbe used as another member of a donor-acceptor pair include DABCYL andthe BLACK HOLE QUENCHER moieties which are available from BiosearchTechnologies, Inc., (Novato, Calif.).

Certain amplicon monitoring procedures most commonly used in connectionwith PCR-based methods rely on the use of DNA binding dyes or probeswhich can be digested by a nuclease activity. In the first instance, theamount of analyte amplicon present in a reaction preferably is monitoredusing fluorescent double-stranded DNA recognizing compounds. This ispossible because the amount of double-stranded amplification productusually exceeds the amount of nucleic acid originally present in thesample to be analyzed. Double-stranded DNA specific dyes may be used inthese monitoring procedures so that, upon excitation with an appropriatewavelength of light, enhanced fluorescence results only if the dye isbound to double-stranded DNA. Preferably, only those dyes are usedwhich, like SYBR Green I (Molecular Probes/Invitrogen Corporation; CA),do not affect the efficiency of the PCR reaction. In an alternativeprocedure, the amplification product is detected using a single-strandedhybridization probe which is labeled with a fluorescent entity. Thefluorescence emission of this entity is quenched by a second label whichis present on the same probe, and which acts as a quenching compound.During the annealing step of the PCR reaction, the probe hybridizes toits target sequence, and, subsequently, during the extension of theprimer, the DNA polymerase having a 5′-3′-exonuclease activity digeststhe hybridization probe such that the fluorescent entity is separatedfrom the quencher compound. After appropriate excitation, fluorescenceemission can be monitored as an indicator of accumulating amplificationproduct.

Synthetic techniques and methods of bonding labels to nucleic acids anddetecting labels are well known in the art (e.g., see Sambrook et al.,Molecular Cloning, A Laboratory Manual, 2nd ed. (Cold Spring HarborLaboratory Press, Cold Spring Harbor, N.Y., 1989), Chapter 10; Nelson etal., U.S. Pat. No. 5,658,737; Woodhead et al., U.S. Pat. No. 5,656,207;Hogan et al., U.S. Pat. No. 5,547,842; Arnold et al., U.S. Pat. No.5,283,174; Kourilsky et al., U.S. Pat. No. 4,581,333), and Becker etal., European Patent App. No. 0 747 706.

Development of Vector-Based Approaches for Complex Curve Analysis

Our experience with real-time nucleic acid amplification indicated thatcurve shapes differed substantially when reactions were initiated usinghigh and very low levels of analyte polynucleotide. More specifically,reactions conducted using high levels of analyte polynucleotide yieldedcurves having generally sigmoid shapes. Conversely, reactions conductedusing very low levels of analyte polynucleotide yielded biphasic curveshaving a baseline that transitioned into a growth phase, wherein thesignal indicating amplicon accumulation increased continuouslythroughout the monitoring period. We further observed that reactionsconducted using very low levels of analyte polynucleotide frequentlyexhibited a “fanning” pattern wherein parallel reactions conducted usingthe same low target levels displayed growth curves with a range ofslopes. An example of nucleic acid amplification reactions that weremonitored as a function of time, and that yielded growth curvesexhibiting “fanning” patterns, can be seen in FIG. 13. Methods foranalyzing growth curves using derivative-based calculations arefrequently challenged when presented with results that exhibit fanning.

Despite possible differences in curve shapes, we discovered thatessentially all growth curves arising from reactions initiated using anyparticular level of analyte polynucleotide substantially shared a commontime at which the amplicon signal emerged from a baseline phase. Stateddifferently, the curves characteristic of amplification reactionsinitiated with a single high level of analyte polynucleotide shared acommon time of emergence (i.e., the end of the baseline phase).Similarly, the curves characteristic of amplification reactionsinitiated with a single low level of analyte polynucleotide shared acommon time of emergence from the baseline phase, even when thedifferent reactions showed evidence of fanning.

Accordingly, it was of interest to develop a mathematical approach foraccurately determining the time at which any particular amplificationreaction transitioned from a baseline phase to a growth phase. Such anapproach would facilitate identification of a common feature of allgrowth curves, and would not require that different curves in theanalysis share similar shapes.

Positive attributes of the vector-based curve analysis methods disclosedherein can be appreciated from FIG. 13, which shows how the influence ofthe fanning effect is reduced when compared with threshold-based methodsof curve analysis. More particularly, the figure shows how the TArcvalues (indicated by the inclined dotted line) vary over a narrow rangecompared with the time values determined when fanned growth curves arerequired to exceed a threshold (indicated by the heavy dashed line).Moreover, it should be apparent that derivative-based curve analysismethods (e.g., based on identifying the maximum of a first or a secondderivative) also would identify points on fanned growth curves thatwould have reduced precision. Notably, the relative positions of thesedifferent features of an example growth curve are illustrated in FIG. 6.Thus, the vector-based methods disclosed herein advantageously yieldmore precise results when compared with either threshold-based curveanalysis methods, or derivative-based curve analysis methods.

Application of the Vector-Based Analysis

Generally speaking, the vector-based methods and algorithms disclosedherein can be applied to the analysis of any curve. However, the methodsare preferably applied to the analysis of “growth curves” that include abaseline phase and a growth phase. FIG. 1A illustrates a biphasic growthcurve that essentially consists of an early baseline phase and a latergrowth phase. The growth phase of the curve is characterized by apositive slope, meaning that the y-component of the curve increases as afunction of time. Significantly, the slope of the growth phase portionof the curve is greater than the slope of the baseline phase. FIG. 1Billustrates a triphasic growth curve that further includes a plateauphase. The slope of the curve within the region of the plateau phase isless than that of the growth phase. To simplify terminology, the growthphase of a triphasic growth curve is alternatively referred to herein asthe “growth phase” or “log-linear” phase. The log-linear phaseconventionally includes the portion of the growth curve wherein ampliconproduction is exponential. In certain respects, the curve illustrated inFIG. 1A can be considered a truncated version of the curve illustratedin FIG. 1B. Truncation of a curve may be mediated by including orotherwise allowing only for analysis of the portion of the curve havinga y-axis value less than a specified amount or percentage of the maximummeasured value.

Each of the curves illustrated in FIGS. 1A-1B includes a transitionbetween the different phases (indicated by dotted circles). Thesetransitions mark brief periods during which the curves cannot beconsidered to be only in one of the three idealized phases (baseline,growth/log-linear, or plateau). Identifying the positions of thetransition phases of growth curves frequently provides valuableinformation, and is one object of the invention. More particularly, itwas of interest to identify the conclusion of the baseline phase, andthe start of the transition into the growth phase or log-linear phase ofa curve.

One aspect of the disclosed method and algorithm relates to performing avector analysis on data that characterizes a measurable result as afunction of time. In this regard, the quantitative method preferablyinvolves a vector analysis for identifying a feature of a growth curvewhich indicates a transition from the baseline phase into the growthphase of a curve. Because there are alternative parameters that can bedetermined in the analysis, the general term “feature” is applied withthe intention of capturing the various alternatives without limitation.Examples of these different parameters include: (1) the angle betweentwo vectors drawn on a growth curve representing experimental data, (2)the rate of change of the angle between two vectors as a function of thetime component of a growth curve representing experimental data, and (3)the magnitude of a third vector which joins the arrowheads of twovectors drawn on a growth curve representing experimental data.

In certain embodiments of the vector analysis, the feature identifiedfor each growth curve relates to the angle between paired sets ofvectors drawn on the curve as described herein. In one embodiment, theidentified feature is the position along the time axis of the curvewhich is associated with the maximum angle between the paired sets ofvectors (i.e., “TArc” herein). In a different embodiment, the identifiedfeature is the position along the time axis of the curve which isassociated with the minimum angle between the paired sets of vectors(i.e., “OTArc” herein).

Vector Analysis

The present invention employs a vector analysis to determine the pointat which a growth curve begins to curve or “inflect” upward. Thisdetermined point, which may be the “TArc” or the “OTArc,” can be used tocreate a calibration curve, or to establish a parameter of anamplification reaction that relates to the amount or concentration of ananalyte polynucleotide in a test sample. This may involve comparing thedetermined time-dependent feature of the growth curve (i.e., the TArc orOTArc) obtained for a test sample with a standard calibration curve. Thevector analysis is most conveniently carried out using growth curveshaving data points distributed over substantially uniform timeintervals. The essential concept underlying the vector analysis isillustrated in FIGS. 2-3, and FIG. 5.

Algorithm Employing Directionally Similar Vectors—TArc

In one embodiment, the vector-based analytical method employs vectorsthat are directionally similar. This means that the vectors share thesame direction in their x-components. Thus, for example, vectors thatare directionally similar will have x-components directed towardincreasing x-values. This is distinguished from the situationcharacterizing directionally opposed vectors, wherein one vector isdirected opposite the other in the x-component.

FIG. 2 illustrates the arrangement of a single paired set ofdirectionally similar vectors drawn on an example growth curve. Asindicated in the figure, a first vector (i.e., vector a) is establishedhaving its origin (i.e., tail terminus) positioned on a first data point(x1,y1) of the growth curve, and its head positioned on a second datapoint (x2,y2) of the same growth curve. The x-component (i.e., the timeor cycle number axis) of the second data point has a value greater thanthe x-component of the first data point (i.e., x2>x1). For the purposeof illustration, if the growth curve is parsed into 5 second intervalsin the time dimension, then the time value of the first data point canbe 0 seconds and the time value of the second data point can be 5seconds. In this illustration the time values of the two data pointswould be spaced apart by one unit (i.e., 5 seconds) in the timedimension of the growth curve. The magnitude of the first vector wouldsimply be the distance separating the first and second data points. They-components of the first and second data points correspond to themagnitudes of the amplicon signals measured at the specified timepoints. A second vector (vector b) is established having its originpositioned on the same first data point (x1,y1) of the growth curve thatwas used for the first vector, and its head positioned on a third datapoint (x3,y3) of the growth curve having an x-component greater than thex-component of the second data point of the first vector (i.e., x3>x2).For example, if the time dimension values of the two data points used toestablish the first vector are separated by one unit on the x-axis, thenthe two data points used to establish the second vector should beseparated on the x-axis by an amount greater than this number.

Preferably, the magnitude of the x-component of the second vector isgreater than the magnitude of the x-component of the first vector, morepreferably by at least 2 fold, still more preferably by at least 5 fold,still yet more preferably by at least 10 fold, and even still yet morepreferably by at least 20 fold. In some highly preferred embodiments,the x-component of the second vector is greater than the x-component ofthe first vector by 25 fold or less, more preferably by 20 fold or less,or even by 15 fold or less. In certain highly preferred embodiments, thex-component of the second vector is greater than the x-component of thefirst vector by 2-25 fold, more preferably by 5-25 fold, more preferablyby 10-20 fold, or still more preferably by 15-20 fold. For example, thex-component of the second vector may be greater than the x-component ofthe first vector by 17 fold. In this latter instance, if one unit in thetime dimension of the growth curve equals 5 seconds, then thex-component of the second vector would be 85 seconds. It should beapparent that the y-component of the third data point corresponds to themagnitude of the amplicon signal measured at the specified time point.

In accordance with the invention, paired sets of two vectors (i.e.,vector a and vector b), are established at regular time intervals, orcycle number intervals, along the growth curve, with each vector of asingle set of paired vectors sharing a common origin (i.e., the datapoint corresponding to the tail of each vector). The magnitudes of thex-components of the first and second vectors in each paired set ofvectors are held constant for the analysis. For example, if themagnitude of the x-component of the first vector in each pair is 5seconds, then the magnitude of the x-component of the second vector ineach pair can be held constant at, for example, 85 seconds. This resultsin a plurality of paired sets of two vectors, with the origins of thedifferent paired sets of vectors being positioned at different timepoints. Table 1 illustrates how the x-component of paired sets ofdirectionally similar vectors would increment in a reiterative processof establishing paired sets of vectors, with “x1” being the value of thex-coordinate of the shared origin, “x2” being the value of thex-coordinate at the head of the first vector, and “x3” being the valueof the x-coordinate at the head of the second vector. FIG. 3 illustratesthe positioning of three sets of vector pairs. Preferably, a pluralityof the regularly spaced time points along the growth curve serve as theorigins of different paired set of vectors. TABLE 1 Incrementing theX-Component of Directionally Similar Vector Sets Increment No. X1 (sec)X2 (sec) X3 (sec) 1 0 5 85 2 5 10 90 3 10 15 95 4 15 20 100 5 20 25 105N n n + 5 n + 85

Once the paired sets of vectors are established, at least one featurecharacterizing the relationship between two vectors of a single set isthen identified or determined. In a preferred embodiment, the angle (ω)between the two vectors is determined using standard mathematicaltechniques that will be familiar to those having an ordinary level ofskill in the art. The determined angle is associated with a time, orcycle number value along the growth curve. For example, the anglebetween two vectors (i.e., vector a and vector b) can be determinedaccording to equation (1).ω=arccos[a·b/(∥a∥ ∥b∥)]  (1)Those having an ordinary level of skill in the art will understand fromequation (1) that the dot product of two vectors has the propertydefined by equation (2), where angle omega (ω) is the angle betweenvector a and vector b (assuming that the two vectors are are non-zero,so the angle between them is well defined). The angle between thevectors has a numerical value in the interval (0, π). For clarity, FIG.4 illustrates the relationship between the calculated values of angleomega (ω) and the remaining portion of a circle. Since the dot productand the norms of the vectors are easily computed, one can use thisformula to calculate the angle between the vectors.a·b=∥a∥ ∥b∥cos(ω)  (2)Thus, each time value along the x-axis of the growth curve can beassociated with an angle value. This conveniently can be accomplishedusing a tabular format.

The angle (ω) between the two vectors will reach a maximum value withinthe time interval encompassing the upward concave inflection of thegrowth curve, and that point on the time axis is said to be the “TArc.”Because the two vectors will be separated by substantial angles at theinitiation and conclusion of the growth phase or log-linear phase of thegrowth curve, and because the time values corresponding to the firstinstance are of greatest interest, it is convenient, but not required toperform the vector analysis on the portion of the growth curve thatprecedes the conclusion of the growth phase of the growth curve. Thiscan be accomplished, for example by using a sliding window vectoranalysis that excludes the portion of the curve characterized by convexcurvature and the plateau phase. In some instances, this may involveanalyzing the portion of the growth curve that represents a fixedpercentage, such as 75% or 85%, of the maximum signal for ampliconproduction. By “sliding window” vector analysis, is meant a reiterativeprocess of establishing vectors and determining the angle therebetween,and then incrementing along the x-axis of the growth curve to repeat theprocess. Determining the point at which ω is maximal over the analyzedportion of the growth curve can be accomplished simply by sorting acolumn of numbers, as will be familiar to those acquainted with commonlyused computer spreadsheet programs, and then identifying the time pointor cycle number associated with the maximum angle value in the column.It is unnecessary to calculate derivatives or slopes of curve portionsto determine the maximal TArc value.

The TArc values determined in this manner can then be used forquantitative analysis of polynucleotide amounts or concentrations. Moreparticularly, once the TArc values are established for amplificationreactions conducted using known amounts of target or calibrator, thosedata points can be saved, plotted on a graph, or otherwise employed toestablish a calibration curve. FIGS. 8-12 represent example calibrationcurves, because the amplification reactions used for obtaining theresults presented therein were conducted using known amounts of theHIV-1 target polynucleotide. Likewise, the TArc value determined for anamplification reaction performed using an unknown starting amount ofanalyte polynucleotide can be compared with a calibration curve todetermine the starting amount of analyte polynucleotide in a sampleundergoing testing.

Preferably, a repetitive analysis is carried out over the portion of thecurve that includes the upward inflection which indicates the transitionfrom the baseline phase into the growth phase of the curve. In thisrepetitive analysis, the origin of the first paired set of vectors islocated at a first position on the growth curve, and the origin of thesecond paired set of vectors is located at the next position on thegrowth curve which corresponds to the next time point. Notably, it isunnecessary to continue the vector analysis over the portion of thecurve that includes the convex curvature which characterizes thetransition from the growth phase into the plateau phase. Thus, thevector analysis can be limited to the portion of the growth curve thatrepresents a constant percentage of the maximum value achieved on they-axis for any particular growth curve that would be adequate to excludethat portion of the curve representing a significant transition into theplateau phase. For example, the vector analysis can be restricted to theportion of the growth curve representing no more than 85%, morepreferably no more than 75% of the maximum value achieved on the y-axisof the curve. This eliminates the convex curvature leading to theplateau phase as a possible TArc solution. Notably, “convex” curvaturerefers to a surface that curves outward, and “concave” curvature refersto a surface that curves like the inner surface of a sphere.

Algorithm Employing Directionally Opposed Vectors—OTArc

The vector-based algorithm described above and illustrated below inExample 1 employed paired sets of two vectors that were directionallysimilar in the x-dimension. More specifically, the directionally similarvectors both extended away from the shared origin in the direction ofincreasing x-values. This arrangement required the vectors to havedifferent magnitudes in the x-dimension to create the opportunity for anangle therebetween as the two vectors incremented along the growthcurve. The algorithm further involved identifying, as a feature of thecurve, the point on the x-axis at which the smaller angle between thetwo vectors became maximal (see FIG. 4). When derived from analysis ofreactions conducted using known quantities of an analyte polynucleotidestandard, the numerical value of this feature could be plotted againstthe log of the input target copy number to create a standard curve.Alternatively, if the feature was derived from analysis of a reactionconducted using a test sample, the determined feature could be comparedwith a standard plot to determine the starting amount or concentrationof analyte polynucleotide present in the test sample.

Another vector-based algorithm falling within the scope of the inventionsimilarly involved a reiterative process of establishing paired sets ofvectors along a growth curve, but employed directionally opposed vectorsinstead of directionally similar vectors. Again in contrast with thealgorithm employing directionally similar vectors, the algorithmemploying directionally opposed vectors did not require both of thevectors in the paired sets to have different magnitudes in thex-dimension, and involved identifying the point along the x-axis atwhich the angle between two vectors became minimal rather than maximal.In accordance with this latter approach, paired sets of two vectors wereestablished such that they were directionally opposed in the x-dimensionrelative to the shared origin of the two vectors. In general, one memberof a pair of directionally opposed vectors extends from the sharedorigin in the direction of decreasing x-values, while the other memberof the pair extends in the direction of increasing x-values.

Key aspects of the algorithm which employs directionally opposedvectors, and its relationship to the algorithm which employsdirectionally similar vectors, can be understood with reference to FIGS.5A-5B. FIG. 5A schematically illustrates how paired sets ofdirectionally similar vectors can be incremented along the x-dimensionof a growth curve, and demonstrates how the angle between the twovectors becomes maximal in the vicinity of the transition between thebaseline and growth phases of the growth curve. FIG. 5B schematicallyillustrates how paired sets of directionally opposed vectors can beincremented along the x-dimension of a growth curve, and demonstrateshow the angle between the two vectors becomes minimal in the vicinity ofthe transition between the baseline and growth phases of the growthcurve. Notably, there is no requirement for the vectors to havedifferent magnitudes of their x-components when using the algorithmbased on directionally opposed vectors, as illustrated in FIG. 5B. Inone preferred embodiment, the magnitudes of the x-components of the twovectors are the same when the analysis is conducted using directionallyopposed vectors. In an alternative preferred embodiment, the magnitudesof the x-components of the two vectors are different from each otherwhen the analysis is conducted using directionally opposed vectors.

As shown in FIG. 5B, and in accordance with the algorithm employingdirectionally opposed vectors, a plurality of paired sets of two vectorsare established at different points along the x-dimension of a growthcurve. Each of the vectors of a single set of vectors shares the sameorigin (x1,y1). Also as indicated in the figure, the vectors areestablished such that their directions are opposed to each other in thex-dimension. When this is the case, the x-coordinate at the head of thefirst vector (i.e., vector a) has a value less than the value of thex-coordinate at shared origin, and the x-coordinate at the head of thesecond vector (i.e., vector b) has a value greater than the value of thex-coordinate of the shared origin. Stated differently, when usingdirectionally opposed vectors x2<x1<x3, where x1 is the x-coordinate ofthe shared origin, where x2 is the x-coordinate at the head of thevector directed toward decreasing x-values, and where x3 is thex-coordinate at the head of the vector directed toward increasingx-values. This arrangement is distinguished from embodiments of theinvention which employ directionally similar vectors, wherein x1<x2<x3(as illustrated in FIG. 2 and FIG. 5A).

Thus, if numerical data representing a growth curve for time-dependentmonitoring of amplicon production in a nucleic acid amplificationreaction is processed by curve-fitting to result in an optimizedequation, that equation can be used to parse the processed growth curveinto arbitrary time intervals. For example, these time intervals couldbe 5 seconds each, 10 seconds each, or other desired time or cyclenumber interval. Continuing with this example, the magnitudes of thex-components of the directionally opposed vectors could be arbitrarilyset to 50 seconds. In such a case, the origin of a first paired set ofvectors could be established at x1=50 seconds, the head of the firstvector would then be positioned at x2=0 seconds, and the head of thedirectionally opposed second vector would be positioned at x3=100seconds. As described above, a second paired set of vectors wouldincrement along the x-dimension of the growth curve such that the originof the second paired set of vectors was established at x1=55 seconds,and the process repeated as desired. Table 2 illustrates how thex-components of paired sets of directionally opposed vectors wouldincrement in the reiterative process of establishing paired sets ofvectors, with “x1” being the value of the x-coordinate of the sharedorigin, “x2” being the value of the x-coordinate at the head of thefirst vector, and “x3” being the value of the x-coordinate at the headof the second vector. TABLE 2 Incrementing the X-Component ofDirectionally Opposed Vector Sets Increment No. X1 (sec) X2 (sec) X3(sec) 1 50 0 100 2 55 5 105 3 60 10 110 4 65 15 115 5 70 20 120 N n n −50 n + 50

The values of the y-coordinates of the vectors preferably are determinedeither directly from experimental data, or more preferably calculatedusing a moving average smoothing function, or a curve-fitting operation.When curve-fitting is used, values of the y-coordinates of the vectorspreferably are calculated by solving optimized equations usingtechniques described herein, or other techniques that will be familiarto those having an ordinary level of skill in the art. With the x- andy-coordinates of the different paired sets of vectors established, theangles between the vectors can be calculated using standard mathematicalapproaches, such as the above-described equation (1), and the calculatedangles associated with different time points along the x-dimension ofthe growth curve. In accordance with the algorithm employingdirectionally opposed vectors, determining the time component associatedwith the origin of the vector pair having the minimum angle willidentify the feature of the curve (i.e., the OTArc) to be plottedagainst the input log target copy number to create a standard curve, orto be compared with a standard curve to identify the amount orconcentration of polynucleotide analyte in a test sample.

Notably, the two approaches schematized in the different panels of FIG.5 identify different features of the same curve. More specifically, theanalysis employing directionally similar vectors identifies a point onthe curve which precedes, at least slightly, the maximum concavecurvature in the transition separating the baseline and growth phases.In contrast, analyses based on the use of directionally opposed vectorsidentify a feature of the curve which occurs slightly later in thex-dimension, and which is associated more closely with the maximumconcave inflection within the transition between the baseline and growthphases. FIG. 6 illustrates this by showing a raw data growth curve(i.e., not processed by any smoothing algorithm) for a nucleic acidamplification reaction, and indicates the identified points on the curvefor TArc, OTArc, the maximum of the second derivative, and the maximumof the first derivative. Of course, the TArc and OTArc values representthe time values on the x-axis associated with the points indicated onthe growth curve.

Smoothing Growth Curve Data Prior to Vector Analysis

To ensure robustness of the disclosed vector analysis algorithms,methods were sought to improve the quality of growth curve data used inthe analyses. More particularly, methods of smoothing the growth curvesusing either moving averages or curve-fitting techniques were used forthis purpose.

Smoothing by Moving Averages

Moving averages were used to minimize the impact of statisticalfluctuations or “noise” in the growth curve data applied equallythroughout the curve. The moving averages smoothing function takes theaverage of a set of values around a particular value in a list. In oneembodiment, a moving average of five sequential data points representingfluorescence measurements was used. This procedure adjusted thefluorescence measurements which subsequently were used as a new data setfor the growth curve. Table 3 illustrates calculation of the movingaverage for a set of seven data points. TABLE 3 Illustrative Calculationof a Moving Average Time Signal Point Value Moving Average 1 A1 : 2 A2 3A3 (A1 + A2 + A3 + A4 + A5)/5 4 A4 (A2 + A3 + A4 + A5 + A6)/5 5 A5 (A3 +A4 + A5 + A6 + A7)/5 6 A6 7 A7

The vector analysis described herein was used to analyze time-dependentfluorescence data that had been obtained in real-time nucleic acidamplification reactions, and that had been smoothed by the movingaverage technique. Notably, the moving average technique did not yieldan equation that defined the growth curve.

Smoothing by Curve-Fitting

Another method of smoothing growth curve data used in the vectoranalysis involved deriving an equation that defined a growth curve usinga procedure referred to as “curve-fitting.” In this approach, one ofseveral candidate equations served as a basic mathematical model for thegrowth curve. Each of the different equations included variables thatcharacterized different growth curve parameters. After estimating thevalues of these variables, using no more than routine modelingprocedures familiar to those having an ordinary level of skill in theart, a reiterative process of modifying the estimated values to obtainan equation defining the curve was executed using commercially availablecurve-fitting software. All of the curve-fitting described hereinparticularly employed the SOLVER program available as an EXCEL add-intool for finding an optimal value for a formula, and equation solvingfrom Microsoft Corporation, (Redmond, Wash.). However, othercommercially available curve-fitting programs also can be used forestablishing a best-fit curve successfully. The curve-fitting proceduresused herein yielded equations that defined a subject growth curve, or aportion thereof.

The equations deduced by curve-fitting were used to parse the growthcurves into time intervals, to determine the magnitude of ampliconsignals as a function of the parsed time intervals, and to draw pairedsets of vectors and determine TArc or OTArc values in accordance withthe above-described vector analysis methods. Curve-fitting of data thatrelates the amount of analyte amplicon synthesized as a function oftime, or cycle number, can be carried out using different mathematicalmodels. Notably, because this procedure yields an equation defining thegrowth curve, it is possible to perform vector analysis usingarbitrarily parsed time intervals. Thus, even if fluorescent readings ina real-time amplification procedure were only collected at 30 secondintervals, the equation derived from the curve-fitting procedure allowsfor parsing into arbitrary intervals, such as 5 second intervals.

Curve-Fitting: Model A

One embodiment of the curve-fitting procedure began with a polynomialequation such as that described by Weusten et al., in Nucleic Acids Res.Vol. 30, No. 6e26, pp. 1-7 (2002), or a modification thereof. The firstmodel used to demonstrate how curve-fitting could be used in connectionwith the invention involved a four-point curve-fit, and was defined byequation (3).R(t)=Y ₀ +a1a2[e ^(a2(t-a3))/(1+e ^(a2(t-a3)))]ln(1+e ^(a2(t-a3)))  (3)

In equation (3), R(t) represents the polynucleotide level (i.e., theamount or concentration) at a particular time point; Y₀ represents thebackground signal (i.e., the baseline fluorescent signal prior to agrowth phase); a1 and a2 are parameters describing growth; and a3defines the location of the curvature marking the transition into thegrowth phase of the curve. Notably, a1a2 is the slope of the log-linearportion of the curve, and so is associated with the rate of ampliconformation. A reiterative process of adjusting the values of thedifferent variables was carried out using the SOLVER program, making thefollowing initial estimates for each of the variables: Y₀ was estimatedto be 0.1 RFU (Relative Fluorescence Units); a1 was estimated to be0.05, and a2 was estimated to be 1. These estimates were held fixed forall calculations. The value of the a3 variable was estimated to be 3minutes less than the time needed to achieve 25% of the maximumfluorescence signal measured during the course of any particularamplification reaction. The initial estimate for the Y₀ variable wasbased on a global minimum function applied to all curves, with thelowest value being defined as zero on the y-axis. The initial estimateapproximated the baseline fluorescence signal, accounting for baselinestabilization and an observed level of fluctuation commonly referred toas “noise.” The initial estimate for a3 was based on the routineobservation that 25% of the maximum fluorescence signal measured duringthe course of any particular amplification reaction ensured that thesignal was in a portion of the curve above the background fluorescencesignal, and that subtracting 3 minutes from this time value ensured aposition nearer to the beginning of the growth phase of the curve.Initial static estimates for the a1 and a2 variables were based on thefinding that SOLVER software was able to optimize final values for a1and a2 when initial estimates for Y₀ and a3 closely approximated theoptimized values for Y₀ and a3. Simply stated, when the initialestimates for Y₀ and a3 closely approximated the final values for Y₀ anda3, the SOLVER software was able to optimize, through a reiterativeprocess, values for a1 and a2 even when initial estimates for a1 and a2were less than ideal.

Since the determination of TArc and OTArc values required analysis ofthe transition from the baseline phase into the growth phase of thesubject curve, it was unnecessary to perform curve-fitting on theportion of a curve represented by, and following the convex transitionfrom the growth phase into the plateau phase. Thus, the procedure wassimplified by performing curve-fitting on only 75% of the growth curvedata. More specifically, only the data representing 0-75% of the maximumamplitude of the measured fluorescent signal measured in the real-timeamplification procedure was used for curve-fitting. Analysis of theupper portion of the curve was not required for calculating either TArcor OTArc values. As indicated above, reiterative computing techniqueswere used to establish values for each of the four variables such thatan equation substantially conforming to the growth curve represented bythe experimental data resulted.

Curve-Fitting: Model B

To prove that success of the vector-based analytical method did notdepend on the use of any particular mathematical model to carry out thecurve-fitting procedure, another mathematical model also was employed.In this second instance, the commercially available TABLECURVE softwarepackage (SYSTAT Software Inc.; Richmond, Calif.) was used to identifyand select an equation that described exemplary real-time nucleic acidamplification curves. The resulting equation, used for mathematicalmodeling (referred to herein as “Model B”), was given by:R(t)=a+b(1−exp(−(t−d*ln(1−2ˆ^((−1/e)))−c)/d))^(ˆe)  (4)In equation (4), R(t) represents the polynucleotide level (i.e., theamount or concentration) at a particular time point. The five parametersin equation (4) were: “a”, “b”, “c”, “d”, and “e”. The variable “t”represented time. Similar to the approach described above, Model B wasoptimized for each data set using the SOLVER program available as anEXCEL add-in tool from Microsoft Corporation (Redmond, Wash.). Initialestimates were used, but were not optimized to the extent describedunder the discussion of Model A.

In accordance with Model B, the data from real-time amplificationreactions representing 85% of the maximum fluorescence signal measuredduring the course of the reaction was used in the analysis. Thissimplification was possible because it was unnecessary to use portionsof the growth curves representing the convex transition into the plateauphase to identify either TArc or OTArc values.

Curve-Fitting: Model C

To demonstrate even further that success of the vector-based analyticalmethod did not depend on the use of any particular mathematical model tocarry out the curve-fitting procedure, yet another mathematical modelalso was employed. As described above, the TABLECURVE software packagewas again used to identify and select an equation that describedexemplary real-time nucleic acid amplification curves. The resultingequation, used for mathematical modeling (referred to herein as “ModelC”), was given by:R(t)=a+b/(1+exp(−(t−d*ln(2ˆ^((1/e))−1)−c)/d))^(ˆe)  (5)In equation (5), R(t) represents the polynucleotide level (i.e., theamount or concentration) at a particular time point. The five parametersin equation (5) were: “a”, “b”, “c”, “d”, and “e”. The variable “t”represented time. Similar to the approach described above, Model C wasoptimized for each data set using the SOLVER program available as anEXCEL add-in tool from Microsoft Corporation (Redmond, Wash.). Initialestimates were used, but were not optimized to the extent describedunder the discussion of Model A.

In accordance with Model C, the data from real-time amplificationreactions representing 80% of the maximum fluorescence signal measuredduring the course of the reaction was used in the analysis. Again, thissimplification was possible because it was unnecessary to use portionsof the growth curves representing the convex transition into the plateauphase to identify either TArc or OTArc values.

Optional Subtraction of a Global Minimum

Those having an ordinary level of skill in the art will appreciate thatvirtually all nucleic acid detection systems are associated with somelevel of detectable background signal in the absence of any nucleic acidamplification. This background may, for example, result from naturalfluorescence characteristics of the vessel containing the reactionmixture being analyzed for appearance of amplicons. When backgroundfluorescence in a procedure is significant, a global minimum subtractionstep preferably is carried out by finding a global minimum, and thensubtracting this value from all data points representing the signal foramplicon detection. When this is done, the resulting signals foramplicon amount present at the different time points can be comparedwith each other directly. Thus, global minimum subtraction facilitatesuniversal and static Y₀ predictions or estimates. It is to be understoodthat a global minimum is the lowest measured value among a collection ofmeasurements.

The advantages of performing the global minimum subtraction areparticularly noteworthy in the context of multiplex amplification anddetection reactions that employ different fluorescent dyes. Typically,different fluorescent dyes are characterized by different levels ofbackground fluorescence.

Apparatus for Implementing the Vector-Based Algorithm

The vector-based algorithms disclosed herein are convenientlyimplemented using a computer or similar processing device (“computer”hereafter). In different preferred embodiments, software ormachine-executable instructions for performing an algorithm can beloaded or otherwise held in a memory component of a freestandingcomputer, or in a memory component of a computer linked to a device usedfor monitoring, preferably as a function of time, the amount of aproduct undergoing analysis. In a highly preferred embodiment, softwarefor executing the vector-based algorithm is held in a memory componentof a computer that is linked to, or that is an integral part of a devicecapable of monitoring the amount of an amplicon present in a reactionmixture as a function of time.

Indeed, either or both of a controller system for controlling areal-time amplification device and/or the detection system of thereal-time amplification device can be coupled to an appropriatelyprogrammed computer which functions to instruct the operation of theseinstruments in accordance with preprogrammed or user input instructions.The computer preferably also can receive data and information from theseinstruments, and interpret, manipulate and report this information tothe user.

In general, the computer typically includes appropriate software forreceiving user instructions, either in the form of user input into a setof parameter fields, or in the form of preprogrammed instructions (e.g.,preprogrammed for a variety of different specific operations). Thesoftware then converts these instructions to appropriate language forinstructing the operation of the real-time amplification controller tocarry out the desired operation. The computer also is capable ofreceiving data from the one or more sensors/detectors included withinthe system, and interprets the data in accordance with the programming.The system preferably includes software that correlates a feature of agrowth curve representing the quantity of amplified copies of thenucleic acid of interest as a function of time, as detected by thedetector, to the number of copies of the nucleic acid of interestpresent in a test sample.

When the computer used for executing the vector-based algorithm is anintegral component of an apparatus for performing and analyzingreal-time nucleic acid amplification reactions, the apparatus preferablycomprises a temperature-controlled incubator, a detection device forcollecting signals, an analyzing device (e.g., a computer or processor)for analyzing signals and an output device for displaying data obtainedor generated by the analyzing device. The analyzing device may beconnected to the temperature-controlled incubator through an inputdevice known in the art, and/or connected to an output device known inthe art for data display. In one embodiment, the temperature-controlledincubator is capable of temperature cycling.

Generally speaking, the various components of an apparatus forperforming the real-time nucleic acid amplification useful in connectionwith the vector-based algorithm will be conventional components thatwill be familiar to those having an ordinary level of skill in the art.The temperature-controlled incubator used to perform and analyzereal-time nucleic acid amplification may be of a conventional designwhich can hold a plurality of reaction tubes, or up to 96 reactionsamples in a temperature-controlled block in standard amplificationreaction tubes or in wells of a multiwell plate. In one aspect, thedetection system is suitable for detecting optical signals from one ormore fluorescent labels. The output of the detection system (e.g.,signals corresponding to those generated during the amplificationreaction) can be fed to the computer for data storage and manipulation.In one embodiment, the system detects multiple different types ofoptical signals, such as multiple different types of fluorescent labelsand has the capabilities of a microplate fluorescence reader. Thedetection system is preferably a multiplexed fluorimeter containing anexcitation light source, which may be a visible light laser or anultraviolet lamp or a halogen lamp or a light-emitting diode (LED), amultiplexer device for distributing the excitation light to theindividual reaction tubes and for receiving fluorescent light from thereaction tubes, a filtering means for separating the fluorescence lightfrom the excitation light by their wavelengths, and a detection meansfor measuring the fluorescence light intensity. Preferably, thedetection system of the temperature-controlled incubator provides abroad detection range that allows flexibility of fluorophore choice,high sensitivity and excellent signal-to-noise ratio. Optical signalsreceived by the detection system are generally converted into signalswhich can be operated on by the processor to provide data which can beviewed by a user on a display of a user device in communication with theprocessor. The user device may comprise a user interface or may be aconventional commercially available computer system with a keyboard andvideo monitor. Examples of data which can be displayed by the userdevice include amplification plots, scatter plots, sample value screensfor all the tubes or reaction vessels in the assembly and for all labelsused, an optical signal intensity screen (e.g., fluorescent signalintensity screen), final call results, text reports, and the like.

Application of the Vector-Based Algorithm to Quantitation ofPolynucleotides

In accordance with one embodiment, a method for quantifying an analyteis described, wherein the analyte is a polynucleotide. The methodcomprises the steps of contacting the analyte with an amplifying agent(and all compounds needed to amplify at least a part of the analyte) andamplifying at least a part of the analyte to result in the formation ofanalyte amplicons. Typically, only a limited region of the analyte isamplified. More specifically, the sequence of the analyte polynucleotideflanked by two amplification oligonucleotides is amplified in thereaction. The amount of amplification product is then determined as afunction of reaction time, and a vector analysis of the time-dependentamplicon formation performed to determine a feature which indicates atranstion from a baseline phase to a growth phase of a growth curvedescribing the reaction. Although the quantitative relationship betweenthe amount of amplification product and the reaction time isconveniently visualized in the form of a growth curve on atwo-dimensional graph, the vector analysis typically is carried out by acomputer or data processor using only numerical data. In one preferredembodiment the amplification product is detected by means offluorescence and the amplification product is formed in a TMA, NASBA,PCR, or SDA reaction. In the context of this invention, determining theamount of amplification product as a function of time does not mean thatthe absolute amount of amplification product needs to be measured.Instead, it is sufficient to determine relative signals, since relativedetermination allows for performance of the vector analysis to determinethe feature of the growth curve, which according to the invention maythen be used to determine the absolute amount or concentration of theinitial analyte polynucleotide present in the amplification reaction.

To illustrate more particularly how the disclosed vector-based algorithmcan be applied to the quantitation of nucleic acids using real-timeamplification methods, a single set of data was selected for assessmentusing either: (1) vector-based analysis of raw data, (2) vector-basedanalysis of data smoothed by calculation of moving averages, or (3)vector-based analysis of data having been produced by curve-fitting ofthe experimental data using mathematical modeling.

Example 1 describes how the vector-based algorithm employingdirectionally similar vectors was used to analyze results from real-timenucleic acid amplification reactions.

EXAMPLE 1 Application of the Vector-Based Analytical Method UsingDirectionally Similar Vectors

A series of TMA reactions were performed using from 5×10¹ to 5×10⁵copies of an HIV-1 subtype B analyte RNA, and a molecular torch specificfor the amplicon. Fluorescence signals indicating amplicon synthesiswere monitored as a function of time, and the results plotted on thegraph shown in FIG. 7. In this procedure, fluorescence readingsindicative of the amount of amplicon present in the amplificationreaction mixture were taken every 19.4 seconds, approximately. Theresult set represented in the figure was processed using the disclosedalgorithm employing directionally similar vectors under differentconditions of data smoothing.

The vector-based algorithm employed in these analyses involvedestablishing paired sets of vectors, with each vector of a set sharing acommon origin along the data points represented on a growth curve. Thevectors within a set had different x-component magnitudes, so that thevectors had different directions in the vicinity of the transitionbetween the baseline and growth phases of the growth curves. To simplifythe analysis, only the data points preceding the transition into theplateau phase of the growth curves (i.e., representing about 75% of themaximum fluorescent reading) were used in the calculations. Anglesbetween the different vector sets were calculated, and the timeassociated with the vector set giving rise to the greatest angleidentified as the TArc. This is illustrated in FIG. 5A. The TArc valueswere then plotted against the log of the input copy number, and theparameters of a best fit line or curve determined. When an internalcalibrator was used, the relationship between target and internalcalibrator was plotted against the log of the input target copy number,and the parameters of the best fit line or curve determined.

Applications of this analytical method to the experimental data, whetherunprocessed or processed to achieve smoothing, are described below. Inall instances, amplification reactions were performed in replicates ofsix, and the results from these replicates used for statisticalanalysis. In no case did a trial conducted in the absence of addedanalyte polynucleotide produce a result that exceeded a minimum rangecriterion necessary to be considered indicative of the presence of theanalyte. Thus, there were no false positives observed in the procedure.

Raw Experimental Data

The vector-based algorithm was applied directly to the analysis of theraw data used to produce the results shown in FIG. 7. TArc values weredetermined for each of the curves generated at each level of analytepolynucleotide tested, and those results plotted on the graph shown inFIG. 8. A summary of the results plotted in the figure appear in Table4. TABLE 4 Summary of the Analysis Performed Using the Vector-BasedAlgorithm on Raw Experimental Data Input Target Amt. Avg. Std. Dev. Avg.Log Std. Dev. Log (copy no.) TArc TArc Copy No. Copy No. 0 NA NA NA NA 5× 10¹ 19.5 1.07 1.67 0.61 5 × 10² 17.5 0.82 2.63 0.13 5 × 10³ 15.5 0.793.93 0.45 5 × 10⁴ 14.4 0.17 4.54 0.10 5 × 10⁵ 12.4 0.53 5.72 0.30NA: Not applicable

As indicated by the results presented in Table 4 and in FIG. 8, therewas a strong relationship between the determined TArc values and the logof the input template copy number. Scatter among the data points plottedin FIG. 8 resulted from variation among the data plotted in the curvesof FIG. 7. Clearly, the disclosed algorithm could be used to process rawexperimental data that had not been smoothed by calculation of movingaverages or by curve-fitting, to yield a relationship between the amountof template input into an amplification reaction and the determined TArcvalue. Parameters of the line determined using the TArc values appear inTable 9.

Smoothing by Moving Averages

The vector-based algorithm was applied to the analysis of data that hadfirst been smoothed by calculation of a moving average over fiveadjacent data points. TArc values were determined for each of replicatecurves generated at each level of analyte polynucleotide tested, andthose results plotted on the graph shown in FIG. 9. A summary of theresults plotted in the figure appear in Table 5. TABLE 5 Summary of theAnalysis Performed Using the Vector-Based Algorithm on Experimental DataSmoothed by 5-Point Moving Average Input Target Amt. Avg. Std. Dev. Avg.Log Std. Dev. Log (copy no.) TArc TArc Copy No. Copy No. 0 NA NA NA NA 5× 10¹ 19.4 1.00 1.58 0.19 5 × 10² 17.4 0.80 2.71 0.15 5 × 10³ 15.5 0.883.95 0.48 5 × 10⁴ 14.3 0.17 4.57 0.09 5 × 10⁵ 12.3 0.52 5.67 0.28NA: Not applicable

As indicated by the results presented in Table 5 and in FIG. 9, therewas a strong relationship between the determined TArc values and the logof the input template copy number. Again, scatter among the data pointsplotted in the figure resulted from variation among the data plotted inthe curves of FIG. 7. Clearly, the disclosed algorithm could be used toprocess raw experimental data that had been smoothed by calculation ofmoving averages to yield a relationship between the amount of templateinput into an amplification reaction and the determined TArc value.Parameters of the line determined using the TArc values appear in Table9.

Smoothing by Curve-Fitting (Model A)

The vector-based algorithm was applied to the analysis of data that hadbeen used to establish an equation by curve-fitting. In a first exampleof this technique, a four-point curve-fit was carried out for each dataset using equation (3), described above. Initial estimates for thedifferent variables are given above in connection with the discussion ofthis mathematical model. A reiterative process of adjusting the valuesof the different variables was carried out to determine an equation foreach curve, also as described above. Thereafter, the growth curverepresented by the equation was parsed into 5 second time units, and thevector analysis conducted using these time points as the origins for aseries of vectors, in accordance with the disclosed algorithm. TArcvalues were determined for each of the replicate curves generated ateach level of analyte polynucleotide tested, and those results plottedon the graph shown in FIG. 10. A summary of the results plotted in thefigure appear in Table 6. TABLE 6 Summary of the Analysis PerformedUsing the Vector-Based Algorithm on Experimental Data Smoothed byCurve-Fitting Model A Input Target Amt. Avg. Std. Dev. Avg. Log Std.Dev. Log (copy no.) TArc TArc Copy No. Copy No. 0 NA NA NA NA 5 × 10¹19.0 0.90 1.64 0.54 5 × 10² 17.2 0.76 2.72 0.46 5 × 10³ 15.3 0.89 3.850.53 5 × 10⁴ 14.1 0.13 4.57 0.08 5 × 10⁵ 12.2 0.46 5.72 0.28NA: Not applicable

As indicated by the results presented in Table 6 and in FIG. 10, therewas a strong relationship between the determined TArc values and the logof the input template copy number. Scatter among the data points plottedin FIG. 10 resulted from variation among the data plotted in the curvesof FIG. 7. Clearly, the disclosed algorithm could be used to processexperimental data that had been smoothed by a first curve-fittingapproach to yield a relationship between the amount of template inputinto an amplification reaction and the determined TArc value. Parametersof the line determined using the TArc values appear in Table 9.

Smoothing by Curve-Fitting (Model B)

The vector-based algorithm was next applied to the analysis of data thathad been used to establish an equation by an alternative curve-fittingoperation. In this second example of the technique, a five-pointcurve-fit was carried out for each data set using equation (4),described above. As indicated above in connection with the discussion ofthis mathematical model, initial estimates for the different variableswere used, and a reiterative process of adjusting the values of thedifferent variables was carried out to determine an equation for eachcurve. Thereafter, the growth curve represented by the equation wasparsed into 5 second time units, and the vector analysis conducted usingthese time points as the origins for a series of vectors, in accordancewith the disclosed algorithm. TArc values were determined for each ofthe replicate curves generated at each level of analyte polynucleotidetested, and those results plotted on the graph shown in FIG. 11. Asummary of the results plotted in the figure appear in Table 7. TABLE 7Summary of the Analysis Performed Using the Vector-Based Algorithm onExperimental Data Smoothed by Curve-Fitting Model B Input Target Amt.Avg. Std. Dev. Avg. Log Std. Dev. Log (copy no.) TArc TArc Copy No. CopyNo. 0 NA NA NA NA 5 × 10¹ 18.9 0.92 1.67 0.55 5 × 10² 17.2 0.80 2.700.48 5 × 10³ 15.3 0.92 3.86 0.55 5 × 10⁴ 14.2 0.18 4.51 0.11 5 × 10⁵12.1 0.53 5.76 0.32NA: Not applicable

As indicated by the results presented in Table 7 and in FIG. 11, therewas a strong relationship between the determined TArc values and the logof the input template copy number. Scatter among the data points plottedin FIG. 11 resulted from variation among the data plotted in the curvesof FIG. 7. Clearly, the disclosed algorithm could be used to processexperimental data that had been smoothed by a first curve-fittingapproach to yield a relationship between the amount of template inputinto an amplification reaction and the determined TArc value. Parametersof the line determined using the TArc values appear in Table 9.

Smoothing by Curve-Fitting (Model C)

The vector-based algorithm was next applied to the analysis of data thathad been used to establish an equation by yet another curve-fittingoperation. In this third example of the technique, a five-pointcurve-fit was carried out for each data set using equation (5),described above. As indicated above in connection with the discussion ofthis mathematical model, initial estimates for the different variableswere used, and a reiterative process of adjusting the values of thedifferent variables was carried out to determine an equation for eachcurve. Thereafter, the growth curve represented by the equation wasparsed into 5 second time units, and the vector analysis conducted usingthese time points as the origins for a series of vectors, in accordancewith the disclosed algorithm. TArc values were determined for each ofthe replicate curves generated at each level of analyte polynucleotidetested, and those results plotted on the graph shown in FIG. 12. Asummary of the results plotted in the figure appear in Table 8. TABLE 8Summary of the Analysis Performed Using the Vector-Based Algorithm onExperimental Data Smoothed by Curve-Fitting Model C Input Target Amt.Avg. Std. Dev. Avg. Log Std. Dev. Log (copy no.) TArc TArc Copy No. CopyNo. 0 NA NA NA NA 5 × 10¹ 19.0 0.91 1.64 0.54 5 × 10² 17.1 0.74 2.740.44 5 × 10³ 15.3 0.89 3.84 0.53 5 × 10⁴ 14.1 0.16 4.53 0.09 5 × 10⁵12.1 0.51 5.74 0.30NA: Not applicable

As indicated by the results presented in Table 8 and in FIG. 12, therewas a strong relationship between the determined TArc values and the logof the input template copy number. Scatter among the data points plottedin FIG. 12 resulted from variation among the data plotted in the curvesof FIG. 7. Clearly, the disclosed algorithm could be used to processexperimental data that had been smoothed by a first curve-fittingapproach to yield a relationship between the amount of template inputinto an amplification reaction and the determined TArc value. Parametersof the line determined using the TArc values appear in Table 9. TABLE 9Parameters of Lines Determined in FIGS. 8-12 Using the Vector-BasedAlgorithm Slope Intercept Test Condition (min/log copy number) (min) Rsquared Raw Data −1.74 22.4 0.937 Moving Average −1.82 22.7 0.958 CurveFit (A) −1.67 21.7 0.928 Curve Fit (B) −1.67 21.7 0.921 Curve Fit (C)−1.68 21.8 0.928

The results summarized in Table 9 indicated that all of the differentanalysis procedures yielded substantially identical results. Thus, thevector-based method was useful for analyzing raw experimental data, aswell as data that had been processed by a smoothing function.

Example 2 describes how the vector-based algorithm employingdirectionally opposed vectors was used to analyze results from real-timenucleic acid amplification reactions. A comparative analysis carried outusing the vector-based algorithm employing directionally similar vectorsis also presented. As demonstrated by the results appearing below, bothof the vector-based algorithms gave substantially equivalent resultseven though the curve features identified by the two approaches weredifferent.

EXAMPLE 2 Application of the Vector-Based Analytical Method UsingDirectionally Opposed Vectors

A single set of results from time-dependent monitoring of nucleic acidamplification reactions were processed in parallel using a computerrunning a program to execute independent algorithms employingdirectionally similar vectors, or directionally opposed vectors. TMAreactions that amplified an HIV-1 analyte polynucleotides were carriedout using analyte polynucleotide amounts that ranged from 0-5×10⁵copies/reaction. All reactions included a fluorescently labeledmolecular torch hybridization probe specific for the HIV-1 amplicon.Fluorescence readings indicating the amounts of amplicon present in thereactions were recorded every 19.4 seconds, approximately. The numericalresults from the amplification reactions were processed for smoothing bya four-point curve fit using the above-described Model A (equation (3)).Initial estimates for the different variables are given above inconnection with the discussion of this mathematical model. A reiterativeprocess of adjusting the values of the different variables was carriedout to determine an optimized equation for each curve, also as describedabove. Thereafter, the growth curve represented by the determinedequation was parsed into 5 second time units, and the vector analysesconducted using these time points as the origins for a series ofvectors.

The algorithm employing directionally opposed vectors used a 50 secondx-component magnitude to establish each member of a plurality of pairedsets of vectors that incremented along the x-dimension of the processedgrowth curves. Thus, x-components of the vectors in this procedureincremented as described in Table 2. In accordance with this algorithm,the identified feature of each curve was the time value (i.e., a pointon the x-axis) at which the angle between vectors among the collectionof paired sets of directionally opposed vectors became minimal (i.e.,the OTArc value). This is illustrated in FIG. 5B. A summary of theseresults appear in Table 10.

The algorithm employing directionally similar vectors used a 5 secondx-component magnitude to establish the first member, and an 85 secondx-component magnitude to establish the second member of a plurality ofpaired sets of vectors that incremented along the x-dimension of theprocessed growth curves. Thus, the x-components of the vectors in theprocedure incremented as described in Table 1. In accordance with thisalgorithm, the identified feature of each curve was the time value(i.e., a point on the x-axis) at which the angle between vectors amongthe collection of paired sets of vectors became maximal (i.e., the TArcvalue). A summary of these results appear in Table 11. TABLE 10 Summaryof the Analysis Performed Using a Vector-Based Algorithm EmployingDirectionally Opposed Vectors Input Target Amt. Avg. Std. Dev. Avg. LogStd. Dev. Log (copy no.) OTArc OTArc Copy No. Copy No. 0 NA NA NA NA 1 ×10¹ 14.0 2.83 0.72 2.02 5 × 10¹ 12.6 0.98 1.71 0.70 5 × 10² 11.0 0.182.89 0.13 5 × 10³ 9.8 0.16 3.72 0.11 5 × 10⁴ 8.4 0.12 4.70 0.09 5 × 10⁵7.2 0.11 5.62 0.08NA: Not applicable

TABLE 11 Summary of the Analysis Performed Using a Vector-BasedAlgorithm Employing Directionally Similar Vectors Input Target Amt. Avg.Std. Dev. Avg. Log Std. Dev. Log (copy no.) TArc TArc Copy No. Copy No.0 NA NA NA NA 1 × 10¹ 13.5 2.81 0.72 2.02 5 × 10¹ 12.1 0.99 1.71 0.71 5× 10² 10.5 0.19 2.90 0.14 5 × 10³ 9.3 0.13 3.72 0.09 5 × 10⁴ 8.0 0.114.70 0.08 5 × 10⁵ 6.7 0.10 5.61 0.07NA: Not applicable

Comparison of the results presented in Table 10 and Table 11 confirmedthat the two vector-based algorithms yielded virtually identicalresults, except that the average values for TArc and OTArc were shiftedin time by about 0.5 minutes, as expected. Clearly, the two algorithmsprovided extraordinarily consistent results, and can be used foranalyzing the results from real-time nucleic acid amplificationreactions. More particularly, either algorithm can be used to establisha standard curve, or determine the amount or concentration of an analytepolynucleotide in a test sample. Moreover, either algorithm can be usedto analyze raw experimental data representing a growth curve, or toanalyze experimental data after processing using a smoothing operation.Exemplary smoothing operations include smoothing by calculating movingaverages, and smoothing by curve-fitting to establish an optimizedequation which can be solved for arbitrarily parsed time increments.

The foregoing description proves that any vector-based algorithm whichidentifies a consistent feature of a growth curve can be used foranalyzing results from real-time nucleic acid amplification reactions toyield standard plots or to quantify analyte polynucleotides in a testsample. Useful algorithms can employ paired sets of directionallysimilar vectors, or paired sets of directionally opposed vectors. Thedifferent members of a single set of vectors can have identical ordifferent magnitudes in a dimension employed for incrementing sets ofvectors along the growth curve undergoing analysis. Moreover, usefulvector-based algorithms can be used to analyze raw experimental results,or results processed by a smoothing function. Examples of preferredsmoothing functions include calculation of moving averages, andcurve-fitting operations.

This invention has been described with reference to a number of specificexamples and embodiments thereof. Of course, a number of differentembodiments of the present invention will suggest themselves to thosehaving ordinary skill in the art upon review of the foregoing detaileddescription. Thus, the true scope of the present invention is to bedetermined upon reference to the appended claims.

1. A method for determining the amount of a nucleic acid in a testsample, comprising the steps of: amplifying a predetermined locus of thenucleic acid in an in vitro amplification reaction to create a nucleicacid amplification product; determining a value proportional to theamount of the nucleic acid amplification product present at differenttimes during the in vitro amplification reaction, whereby there iscreated a collection of time-dependent values that collectively define agrowth curve; performing a vector analysis on at least a portion of saidgrowth curve to identify a time-dependent feature thereof, said vectoranalysis comprising the steps of, (a) establishing a plurality of pairsof first and second vectors at different points on a time dimension ofthe growth curve using said collection of time-dependent values, whereineach vector of a single pair of vectors among said plurality has thesame origin, and wherein the head of the first vector and the head ofthe second vector of said single pair of vectors among said pluralityare each positioned at different points on the growth curve, and (b)identifying said time-dependent feature of said growth curve using theplurality of pairs of first and second vectors established in step (a);and determining from said time-dependent feature of said growth curvethe amount of the nucleic acid in the test sample.
 2. The method ofclaim 1, wherein step (b) of the vector analysis comprises calculatingfor each pair of vectors among said plurality the value of a parameterdependent on said pair of vectors.
 3. The method of claim 2, whereinsaid parameter is an angle between vectors.
 4. The method of claim 3,wherein step (b) of the vector analysis further comprises identifying aposition on said growth curve at which said angle between vectors is aminimum angle.
 5. The method of claim 3, wherein step (b) of the vectoranalysis further comprises identifying a position on said growth curveat which said angle between vectors is a maximum angle.
 6. The method ofclaim 1, wherein each vector in said single pair of vectors has adifferent magnitude in said time dimension of said growth curve, saidvectors being directionally similar vectors.
 7. The method of claim 1,wherein each vector in said single pair of vectors has a differentmagnitude in said time dimension of said growth curve, said vectorsbeing directionally opposed vectors.
 8. The method of claim 1, whereineach vector in said single pair of vectors has the same magnitude insaid time dimension of said growth curve, said vectors beingdirectionally opposed vectors.
 9. The method of claim 1, furthercomprising a processing step before the step for performing the vectoranalysis, said processing step comprising processing said collection oftime-dependent values of the first determining step by a smoothingfunction to result in smoothed numerical data, and wherein thecollection of time-dependent values used in step (a) of the vectoranalysis is the smoothed numerical data resulting from the processingstep.
 10. The method of claim 9, wherein the smoothing function in theprocessing step is selected from the group consisting of a movingaverage smoothing function, and a curve-fitting smoothing function. 11.The method of claim 9, wherein step (b) of the vector analysis comprisescalculating for each pair of vectors among said plurality the value of aparameter dependent on said pair of vectors.
 12. The method of claim 11,wherein said parameter is an angle between vectors.
 13. The method ofclaim 9, wherein each vector in said single pair of vectors has adifferent magnitude in said time dimension of said growth curve, saidvectors being directionally similar vectors.
 14. The method of claim 13,wherein step (b) of the vector analysis comprises calculating for eachpair of vectors among said plurality the value of a parameter dependenton said pair of vectors.
 15. The method of claim 14, wherein saidparameter is an angle between vectors.
 16. The method of claim 9,wherein each vector in said single pair of vectors has a differentmagnitude in said time dimension of said growth curve, said vectorsbeing directionally opposed vectors.
 17. The method of claim 16, whereinstep (b) of the vector analysis comprises calculating for each pair ofvectors among said plurality the value of a parameter dependent on saidpair of vectors.
 18. The method of claim 17, wherein said parameter isan angle between vectors.
 19. The method of claim 9, wherein each vectorin said single pair of vectors has the same magnitude in said timedimension of said growth curve, said vectors being directionally opposedvectors.
 20. The method of claim 19, wherein step (b) of the vectoranalysis comprises calculating for each pair of vectors among saidplurality the value of a parameter dependent on said pair of vectors.21. The method of claim 20, wherein said parameter is an angle betweenvectors.
 22. The method of claim 1, wherein said in vitro amplificationreaction in the amplifying step is an isothermal in vitro amplificationreaction.
 23. The method of claim 22, wherein the value proportional tothe amount of the nucleic acid amplification product in the firstdetermining step is a fluorescence value.
 24. The method of claim 23,wherein the step for determining from said time-dependent feature ofsaid growth curve comprises comparing said time-dependent feature ofsaid growth curve with a standard calibration curve.
 25. The method ofclaim 24, wherein the step for performing the vector analysis isautomated by a computer.
 26. A method for quantifying an amount of anucleic acid in a test sample, comprising the steps of: contacting thetest sample with an amplifying agent; amplifying a predetermined locusof the nucleic acid in an in vitro amplification reaction to create anucleic acid amplification product; determining a value proportional tothe amount of the nucleic acid amplification product present atdifferent times during the amplification reaction; optionally processingthe determined values using a smoothing function to result in processedvalues; performing a vector analysis on either (a) the determined valuesif the optional processing step is omitted or (b) the processed valuesif the optional processing step is included, to identify atime-dependent feature thereof; and calculating from said time-dependentfeature the amount of the nucleic acid in the test sample.
 27. A methodof identifying a feature on a curve, comprising the steps of: (a)obtaining numerical data for the curve; (b) establishing a plurality ofpairs of first and second vectors at different points on a firstdimension of the curve using the numerical data, wherein each vector ofa single pair of vectors among said plurality has the same origin, andwherein the head of the first vector and the head of the second vectorof said single pair of vectors among said plurality are each positionedat different points on the curve; and (c) identifying said feature onsaid curve using the plurality of pairs of first and second vectorsestablished in step (b), wherein steps (b)-(c) are automated by acomputer.
 28. The method of claim 27, wherein step (c) comprisescalculating for each pair of vectors among said plurality the value of aparameter dependent on said pair of vectors.
 29. The method of claim 28,wherein said parameter is an angle between vectors.
 30. The method ofclaim 29, wherein step (c) further comprises identifying a position onsaid curve at which said angle between vectors is a minimum angle. 31.The method of claim 29, wherein step (c) further comprises identifying aposition on said curve at which said angle between vectors is a maximumangle.
 32. The method of claim 27, wherein each vector in said singlepair of vectors has a different magnitude in said first dimension ofsaid curve, said vectors being directionally similar vectors.
 33. Themethod of claim 27, wherein each vector in said single pair of vectorshas a different magnitude in said first dimension of said curve, saidvectors being directionally opposed vectors.
 34. The method of claim 27,wherein each vector in said single pair of vectors has the samemagnitude in said first dimension of said curve, said vectors beingdirectionally opposed vectors.
 35. The method of claim 27, furthercomprising a step for processing the numerical data obtained in step (a)by a smoothing function to result in smoothed numerical data, andwherein the numerical data used in step (b) is the smoothed numericaldata resulting from the processing step.
 36. The method of claim 35,wherein the smoothing function in the processing step is selected fromthe group consisting of a moving average smoothing function, and acurve-fitting smoothing function.
 37. The method of claim 35, whereinstep (c) comprises calculating for each pair of vectors among saidplurality the value of a parameter dependent on said pair of vectors.38. The method of claim 37, wherein said parameter is an angle betweenvectors.
 39. The method of claim 35, wherein each vector in said singlepair of vectors has a different magnitude in said first dimension ofsaid curve, said vectors being directionally similar vectors.
 40. Themethod of claim 39, wherein step (c) comprises calculating for each pairof vectors among said plurality the value of a parameter dependent onsaid pair of vectors.
 41. The method of claim 40, wherein said parameteris an angle between vectors.
 42. The method of claim 35, wherein eachvector in said single pair of vectors has a different magnitude in saidfirst dimension of said curve, said vectors being directionally opposedvectors.
 43. The method of claim 42, wherein step (c) comprisescalculating for each pair of vectors among said plurality the value of aparameter dependent on said pair of vectors.
 44. The method of claim 43,wherein said parameter is an angle between vectors.
 45. The method ofclaim 35, wherein each vector in said single pair of vectors has thesame magnitude in said first dimension of said curve, said vectors beingdirectionally opposed vectors.
 46. The method of claim 45, wherein step(c) comprises calculating for each pair of vectors among said pluralitythe value of a parameter dependent on said pair of vectors.
 47. Themethod of claim 46, wherein said parameter is an angle between vectors.48. A system for determining an initial amount of analyte polynucleotidepresent in a test sample using time-dependent monitoring of an in vitronucleic acid amplification reaction, comprising: a means for obtaining adata set of values proportional to the amount of a nucleic acidamplification product present in the in vitro nucleic acid amplificationreaction as a function of time, said data set defining a growth curve; ameans for processing the data set defining the growth curve, comprisinga memory device containing instructions for performing a vector analysisof the growth curve to yield a processed result that determines theinitial amount of analyte polynucleotide present in the test sample; anda means for reporting the processed result.